---
title: Agentic RAG with LanceDB
---

## Code

```python
rom agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.lancedb import LanceDb, SearchType

knowledge = Knowledge(
    vector_db=LanceDb(
        table_name="recipes",
        uri="tmp/lancedb",
        search_type=SearchType.vector,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

knowledge.add_content(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)

agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    knowledge=knowledge,
    search_knowledge=True,
    markdown=True,
)
agent.print_response(
    "How do I make chicken and galangal in coconut milk soup", stream=True
)

```

## Usage

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Set your API key">
    ```bash
    export OPENAI_API_KEY=xxx
    ```
  </Step>

  <Step title="Install libraries">
    ```bash
    pip install -U openai lancedb tantivy pypdf sqlalchemy agno
    ```
  </Step>

  <Step title="Run Agent">
    <CodeGroup>
    ```bash Mac
    python cookbook/agent_concepts/rag/agentic_rag_lancedb.py
    ```

    ```bash Windows
    python cookbook/agents/rag/agentic_rag_lancedb.py
    ```
    </CodeGroup>
  </Step>
</Steps>